Overview

Dataset statistics

Number of variables20
Number of observations124101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.9 MiB
Average record size in memory168.0 B

Variable types

Categorical13
Numeric5
DateTime2

Alerts

animal_id has a high cardinality: 110926 distinct values High cardinality
name has a high cardinality: 19748 distinct values High cardinality
breed has a high cardinality: 2629 distinct values High cardinality
color has a high cardinality: 595 distinct values High cardinality
found_location has a high cardinality: 53703 distinct values High cardinality
age_month_in is highly correlated with age_year_inHigh correlation
age_month_out is highly correlated with age_year_outHigh correlation
age_year_in is highly correlated with age_month_inHigh correlation
age_year_out is highly correlated with age_month_outHigh correlation
age_month_in is highly correlated with age_year_inHigh correlation
age_month_out is highly correlated with age_year_outHigh correlation
age_year_in is highly correlated with age_month_inHigh correlation
age_year_out is highly correlated with age_month_outHigh correlation
age_month_in is highly correlated with age_year_inHigh correlation
age_month_out is highly correlated with age_year_outHigh correlation
age_year_in is highly correlated with age_month_inHigh correlation
age_year_out is highly correlated with age_month_outHigh correlation
intake_type is highly correlated with neutered_intakeHigh correlation
sex is highly correlated with neutered_intakeHigh correlation
outcome_subtype is highly correlated with outcome_type and 1 other fieldsHigh correlation
outcome_type is highly correlated with outcome_subtype and 1 other fieldsHigh correlation
neutered_outcome is highly correlated with outcome_subtype and 1 other fieldsHigh correlation
neutered_intake is highly correlated with intake_type and 1 other fieldsHigh correlation
animal_type is highly correlated with sex and 2 other fieldsHigh correlation
sex is highly correlated with animal_type and 2 other fieldsHigh correlation
intake_type is highly correlated with animal_type and 2 other fieldsHigh correlation
neutered_intake is highly correlated with animal_type and 2 other fieldsHigh correlation
neutered_outcome is highly correlated with outcome_type and 1 other fieldsHigh correlation
age_month_in is highly correlated with age_year_inHigh correlation
age_month_out is highly correlated with age_year_outHigh correlation
age_year_in is highly correlated with age_month_inHigh correlation
age_year_out is highly correlated with age_month_outHigh correlation
outcome_type is highly correlated with neutered_outcome and 1 other fieldsHigh correlation
outcome_subtype is highly correlated with neutered_outcome and 1 other fieldsHigh correlation
animal_id is uniformly distributed Uniform

Reproduction

Analysis started2022-04-26 16:41:37.955297
Analysis finished2022-04-26 16:50:07.302031
Duration8 minutes and 29.35 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

animal_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct110926
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
A721033
 
33
A718223
 
14
A718877
 
12
A706536
 
11
A761266
 
9
Other values (110921)
124022 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100838 ?
Unique (%)81.3%

Sample

1st rowA006100
2nd rowA006100
3rd rowA006100
4th rowA047759
5th rowA134067

Common Values

ValueCountFrequency (%)
A72103333
 
< 0.1%
A71822314
 
< 0.1%
A71887712
 
< 0.1%
A70653611
 
< 0.1%
A7612669
 
< 0.1%
A7170539
 
< 0.1%
A7160189
 
< 0.1%
A7378149
 
< 0.1%
A6164449
 
< 0.1%
A7335948
 
< 0.1%
Other values (110916)123978
99.9%

Length

2022-04-26T18:50:07.362868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a72103333
 
< 0.1%
a71822314
 
< 0.1%
a71887712
 
< 0.1%
a70653611
 
< 0.1%
a7612669
 
< 0.1%
a7170539
 
< 0.1%
a7160189
 
< 0.1%
a7378149
 
< 0.1%
a6164449
 
< 0.1%
a6706128
 
< 0.1%
Other values (110916)123978
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

name
Categorical

HIGH CARDINALITY

Distinct19748
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Unknown
39065 
Max
 
564
Bella
 
523
Luna
 
494
Rocky
 
383
Other values (19743)
83072 

Length

Max length24
Median length7
Mean length6.270650519
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10938 ?
Unique (%)8.8%

Sample

1st rowScamp
2nd rowScamp
3rd rowScamp
4th rowOreo
5th rowBandit

Common Values

ValueCountFrequency (%)
Unknown39065
31.5%
Max564
 
0.5%
Bella523
 
0.4%
Luna494
 
0.4%
Rocky383
 
0.3%
Daisy365
 
0.3%
Princess331
 
0.3%
Charlie326
 
0.3%
Coco324
 
0.3%
Buddy320
 
0.3%
Other values (19738)81406
65.6%

Length

2022-04-26T18:50:07.472574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown39065
30.5%
max626
 
0.5%
bella574
 
0.4%
luna552
 
0.4%
daisy454
 
0.4%
charlie446
 
0.3%
rocky416
 
0.3%
blue394
 
0.3%
lucy383
 
0.3%
coco366
 
0.3%
Other values (14225)84966
66.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

animal_type
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Dog
70438 
Cat
46448 
Other
 
6607
Bird
 
586
Livestock
 
22

Length

Max length9
Median length3
Mean length3.112263398
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDog
2nd rowDog
3rd rowDog
4th rowDog
5th rowDog

Common Values

ValueCountFrequency (%)
Dog70438
56.8%
Cat46448
37.4%
Other6607
 
5.3%
Bird586
 
0.5%
Livestock22
 
< 0.1%

Length

2022-04-26T18:50:07.576297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T18:50:07.646110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
dog70438
56.8%
cat46448
37.4%
other6607
 
5.3%
bird586
 
0.5%
livestock22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

breed
Categorical

HIGH CARDINALITY

Distinct2629
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Domestic Shorthair Mix
30978 
Pit Bull Mix
8406 
Domestic Shorthair
7006 
Labrador Retriever Mix
6861 
Chihuahua Shorthair Mix
 
6237
Other values (2624)
64613 

Length

Max length54
Median length22
Mean length19.22688778
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique994 ?
Unique (%)0.8%

Sample

1st rowSpinone Italiano Mix
2nd rowSpinone Italiano Mix
3rd rowSpinone Italiano Mix
4th rowDachshund
5th rowShetland Sheepdog

Common Values

ValueCountFrequency (%)
Domestic Shorthair Mix30978
25.0%
Pit Bull Mix8406
 
6.8%
Domestic Shorthair7006
 
5.6%
Labrador Retriever Mix6861
 
5.5%
Chihuahua Shorthair Mix6237
 
5.0%
Domestic Medium Hair Mix3115
 
2.5%
German Shepherd Mix3003
 
2.4%
Bat Mix1755
 
1.4%
Domestic Longhair Mix1540
 
1.2%
Australian Cattle Dog Mix1511
 
1.2%
Other values (2619)53689
43.3%

Length

2022-04-26T18:50:07.763302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mix90753
26.6%
shorthair46552
13.7%
domestic43722
12.8%
bull11248
 
3.3%
pit10871
 
3.2%
retriever10090
 
3.0%
labrador9923
 
2.9%
chihuahua8875
 
2.6%
shepherd6165
 
1.8%
terrier5874
 
1.7%
Other values (1855)96649
28.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

color
Categorical

HIGH CARDINALITY

Distinct595
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Black/White
13033 
Black
10349 
Brown Tabby
 
7035
Brown
 
5422
White
 
4381
Other values (590)
83881 

Length

Max length27
Median length10
Mean length9.581204019
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)0.1%

Sample

1st rowYellow/White
2nd rowYellow/White
3rd rowYellow/White
4th rowTricolor
5th rowBrown/White

Common Values

ValueCountFrequency (%)
Black/White13033
 
10.5%
Black10349
 
8.3%
Brown Tabby7035
 
5.7%
Brown5422
 
4.4%
White4381
 
3.5%
Brown/White4104
 
3.3%
Tan/White3786
 
3.1%
Brown Tabby/White3646
 
2.9%
Orange Tabby3427
 
2.8%
White/Black3419
 
2.8%
Other values (585)65499
52.8%

Length

2022-04-26T18:50:07.897943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brown19793
 
12.8%
tabby14603
 
9.4%
black/white13033
 
8.4%
black11306
 
7.3%
tabby/white6836
 
4.4%
blue5819
 
3.8%
orange5275
 
3.4%
white4381
 
2.8%
brown/white4104
 
2.7%
tan/white3786
 
2.4%
Other values (371)65598
42.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sex
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Male
59450 
Female
54420 
Unknown
10231 

Length

Max length7
Median length6
Mean length5.124350328
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male59450
47.9%
Female54420
43.9%
Unknown10231
 
8.2%

Length

2022-04-26T18:50:08.019617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T18:50:08.093420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
male59450
47.9%
female54420
43.9%
unknown10231
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

intake_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Stray
86448 
Owner Surrender
24600 
Public Assist
 
7618
Wildlife
 
4895
Abandoned
 
283

Length

Max length18
Median length5
Mean length7.627714523
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic Assist
2nd rowPublic Assist
3rd rowStray
4th rowOwner Surrender
5th rowPublic Assist

Common Values

ValueCountFrequency (%)
Stray86448
69.7%
Owner Surrender24600
 
19.8%
Public Assist7618
 
6.1%
Wildlife4895
 
3.9%
Abandoned283
 
0.2%
Euthanasia Request257
 
0.2%

Length

2022-04-26T18:50:08.174203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T18:50:08.256982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
stray86448
55.2%
owner24600
 
15.7%
surrender24600
 
15.7%
public7618
 
4.9%
assist7618
 
4.9%
wildlife4895
 
3.1%
abandoned283
 
0.2%
euthanasia257
 
0.2%
request257
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

intake_condition
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Normal
107936 
Injured
 
6638
Sick
 
5076
Nursing
 
3524
Aged
 
430
Other values (5)
 
497

Length

Max length8
Median length6
Mean length5.992506104
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowInjured

Common Values

ValueCountFrequency (%)
Normal107936
87.0%
Injured6638
 
5.3%
Sick5076
 
4.1%
Nursing3524
 
2.8%
Aged430
 
0.3%
Other229
 
0.2%
Feral108
 
0.1%
Pregnant77
 
0.1%
Medical63
 
0.1%
Behavior20
 
< 0.1%

Length

2022-04-26T18:50:08.385146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T18:50:08.469937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
normal107936
87.0%
injured6638
 
5.3%
sick5076
 
4.1%
nursing3524
 
2.8%
aged430
 
0.3%
other229
 
0.2%
feral108
 
0.1%
pregnant77
 
0.1%
medical63
 
0.1%
behavior20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

neutered_intake
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
no
78425 
yes
35445 
Unknown
10231 

Length

Max length7
Median length2
Mean length2.697818712
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
no78425
63.2%
yes35445
28.6%
Unknown10231
 
8.2%

Length

2022-04-26T18:50:08.580641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T18:50:08.648460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no78425
63.2%
yes35445
28.6%
unknown10231
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

neutered_outcome
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
yes
82914 
no
31016 
Unknown
10171 

Length

Max length7
Median length3
Mean length3.077904288
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes82914
66.8%
no31016
 
25.0%
Unknown10171
 
8.2%

Length

2022-04-26T18:50:08.723259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T18:50:08.785094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
yes82914
66.8%
no31016
 
25.0%
unknown10171
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age_month_in
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.7374271
Minimum0
Maximum300
Zeros754
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-04-26T18:50:08.872859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.46
Q12
median12
Q324
95-th percentile108
Maximum300
Range300
Interquartile range (IQR)22

Descriptive statistics

Standard deviation34.49490764
Coefficient of variation (CV)1.394442013
Kurtosis5.640683047
Mean24.7374271
Median Absolute Deviation (MAD)11.08
Skewness2.28638501
Sum3069939.441
Variance1189.898653
MonotonicityNot monotonic
2022-04-26T18:50:08.997525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1221793
17.6%
2419029
15.3%
111910
 
9.6%
367448
 
6.0%
26732
 
5.4%
484452
 
3.6%
0.924414
 
3.6%
604061
 
3.3%
0.693615
 
2.9%
33276
 
2.6%
Other values (38)37371
30.1%
ValueCountFrequency (%)
0754
 
0.6%
0.0329635
 
0.5%
0.0657478
 
0.4%
0.0986578
 
0.5%
0.1314324
 
0.3%
0.1643180
 
0.1%
0.1972305
 
0.2%
0.231910
1.5%
0.462498
2.0%
0.693615
2.9%
ValueCountFrequency (%)
3001
 
< 0.1%
2881
 
< 0.1%
2761
 
< 0.1%
2645
 
< 0.1%
2521
 
< 0.1%
24019
 
< 0.1%
22827
 
< 0.1%
21647
 
< 0.1%
20482
0.1%
192140
0.1%

age_month_out
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.17343505
Minimum0
Maximum300
Zeros185
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-04-26T18:50:09.122192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.69
Q13
median12
Q324
95-th percentile108
Maximum300
Range300
Interquartile range (IQR)21

Descriptive statistics

Standard deviation34.57216546
Coefficient of variation (CV)1.373359075
Kurtosis5.609726356
Mean25.17343505
Median Absolute Deviation (MAD)11
Skewness2.288366245
Sum3124048.463
Variance1195.234625
MonotonicityNot monotonic
2022-04-26T18:50:09.238389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1222059
17.8%
2418916
15.2%
214994
12.1%
367573
 
6.1%
35926
 
4.8%
15332
 
4.3%
484426
 
3.6%
604087
 
3.3%
44012
 
3.2%
53086
 
2.5%
Other values (38)33690
27.1%
ValueCountFrequency (%)
0185
 
0.1%
0.0329264
 
0.2%
0.0657347
 
0.3%
0.0986354
 
0.3%
0.1314235
 
0.2%
0.1643158
 
0.1%
0.1972237
 
0.2%
0.231510
1.2%
0.462029
1.6%
0.692123
1.7%
ValueCountFrequency (%)
3001
 
< 0.1%
2881
 
< 0.1%
2761
 
< 0.1%
2645
 
< 0.1%
2521
 
< 0.1%
24019
 
< 0.1%
22827
 
< 0.1%
21649
 
< 0.1%
20485
0.1%
192140
0.1%

age_year_in
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.061460475
Minimum0
Maximum25
Zeros754
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-04-26T18:50:09.353082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0384
Q10.1667
median1
Q32
95-th percentile9
Maximum25
Range25
Interquartile range (IQR)1.8333

Descriptive statistics

Standard deviation2.874569977
Coefficient of variation (CV)1.39443371
Kurtosis5.640721199
Mean2.061460475
Median Absolute Deviation (MAD)0.9232
Skewness2.286394016
Sum255829.3064
Variance8.263152555
MonotonicityNot monotonic
2022-04-26T18:50:09.470767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
121793
17.6%
219029
15.3%
0.083311910
 
9.6%
37448
 
6.0%
0.16676732
 
5.4%
44452
 
3.6%
0.07684414
 
3.6%
54061
 
3.3%
0.05763615
 
2.9%
0.253276
 
2.6%
Other values (38)37371
30.1%
ValueCountFrequency (%)
0754
 
0.6%
0.0027635
 
0.5%
0.0055478
 
0.4%
0.0082578
 
0.5%
0.011324
 
0.3%
0.0137180
 
0.1%
0.0164305
 
0.2%
0.01921910
1.5%
0.03842498
2.0%
0.05763615
2.9%
ValueCountFrequency (%)
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
225
 
< 0.1%
211
 
< 0.1%
2019
 
< 0.1%
1927
 
< 0.1%
1847
 
< 0.1%
1782
0.1%
16140
0.1%

age_year_out
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.09779371
Minimum0
Maximum25
Zeros185
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-04-26T18:50:09.585460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0576
Q10.25
median1
Q32
95-th percentile9
Maximum25
Range25
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation2.881008618
Coefficient of variation (CV)1.373351728
Kurtosis5.609761023
Mean2.09779371
Median Absolute Deviation (MAD)0.9167
Skewness2.288374537
Sum260338.2972
Variance8.300210655
MonotonicityNot monotonic
2022-04-26T18:50:09.704142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
122059
17.8%
218916
15.2%
0.166714994
12.1%
37573
 
6.1%
0.255926
 
4.8%
0.08335332
 
4.3%
44426
 
3.6%
54087
 
3.3%
0.33334012
 
3.2%
0.41673086
 
2.5%
Other values (38)33690
27.1%
ValueCountFrequency (%)
0185
 
0.1%
0.0027264
 
0.2%
0.0055347
 
0.3%
0.0082354
 
0.3%
0.011235
 
0.2%
0.0137158
 
0.1%
0.0164237
 
0.2%
0.01921510
1.2%
0.03842029
1.6%
0.05762123
1.7%
ValueCountFrequency (%)
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
225
 
< 0.1%
211
 
< 0.1%
2019
 
< 0.1%
1927
 
< 0.1%
1849
 
< 0.1%
1785
0.1%
16140
0.1%
Distinct87660
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2013-10-01 07:51:00
Maximum2021-03-03 17:13:00
2022-04-26T18:50:09.827320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:50:09.961983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct102583
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2013-10-01 09:31:00
Maximum2021-03-03 17:19:00
2022-04-26T18:50:10.108590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:50:10.249214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

outcome_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Adoption
55201 
Transfer
36608 
Return to Owner
21446 
Euthanasia
8335 
Died
 
1154
Other values (5)
 
1357

Length

Max length15
Median length8
Mean length9.31169773
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturn to Owner
2nd rowReturn to Owner
3rd rowReturn to Owner
4th rowTransfer
5th rowReturn to Owner

Common Values

ValueCountFrequency (%)
Adoption55201
44.5%
Transfer36608
29.5%
Return to Owner21446
 
17.3%
Euthanasia8335
 
6.7%
Died1154
 
0.9%
Rto-Adopt691
 
0.6%
Disposal561
 
0.5%
Missing69
 
0.1%
Relocate21
 
< 0.1%
Unknown15
 
< 0.1%

Length

2022-04-26T18:50:10.374878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-26T18:50:10.447683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
adoption55201
33.1%
transfer36608
21.9%
return21446
 
12.8%
to21446
 
12.8%
owner21446
 
12.8%
euthanasia8335
 
5.0%
died1154
 
0.7%
rto-adopt691
 
0.4%
disposal561
 
0.3%
missing69
 
< 0.1%
Other values (2)36
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

outcome_subtype
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Unknown
66965 
Partner
30721 
Foster
10881 
Rabies Risk
 
3654
Suffering
 
3225
Other values (19)
8655 

Length

Max length19
Median length7
Mean length6.948614435
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowPartner
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown66965
54.0%
Partner30721
24.8%
Foster10881
 
8.8%
Rabies Risk3654
 
2.9%
Suffering3225
 
2.6%
SCRP3208
 
2.6%
Snr2667
 
2.1%
In Kennel595
 
0.5%
Aggressive538
 
0.4%
Offsite383
 
0.3%
Other values (14)1264
 
1.0%

Length

2022-04-26T18:50:10.583321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown66965
51.9%
partner30721
23.8%
foster11176
 
8.7%
rabies3654
 
2.8%
risk3654
 
2.8%
suffering3225
 
2.5%
scrp3208
 
2.5%
snr2667
 
2.1%
in912
 
0.7%
kennel595
 
0.5%
Other values (18)2168
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

days_in_shelter
Real number (ℝ≥0)

Distinct25942
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.3328378
Minimum0
Maximum2654.11
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-04-26T18:50:10.695529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.88
Q112.03
median17.97
Q345.97
95-th percentile690.02
Maximum2654.11
Range2654.11
Interquartile range (IQR)33.94

Descriptive statistics

Standard deviation286.0113022
Coefficient of variation (CV)2.501567422
Kurtosis18.61655153
Mean114.3328378
Median Absolute Deviation (MAD)8.11
Skewness4.064489454
Sum14188819.5
Variance81802.46497
MonotonicityNot monotonic
2022-04-26T18:50:10.823693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.95139
 
0.1%
13.99139
 
0.1%
15.85133
 
0.1%
15.9132
 
0.1%
14.96130
 
0.1%
14.92130
 
0.1%
13.95130
 
0.1%
13.96128
 
0.1%
15.95127
 
0.1%
14.95126
 
0.1%
Other values (25932)122787
98.9%
ValueCountFrequency (%)
011
 
< 0.1%
0.0137
< 0.1%
0.0230
< 0.1%
0.0327
< 0.1%
0.0423
< 0.1%
0.0529
< 0.1%
0.0636
< 0.1%
0.0730
< 0.1%
0.0837
< 0.1%
0.0933
< 0.1%
ValueCountFrequency (%)
2654.111
< 0.1%
2650.981
< 0.1%
2613.081
< 0.1%
2596.771
< 0.1%
25921
< 0.1%
2589.071
< 0.1%
2559.771
< 0.1%
2547.831
< 0.1%
2543.091
< 0.1%
2541.011
< 0.1%

found_location
Categorical

HIGH CARDINALITY

Distinct53703
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Austin (TX)
22851 
Travis (TX)
 
1995
Outside Jurisdiction
 
1529
7201 Levander Loop in Austin (TX)
 
811
Manor (TX)
 
620
Other values (53698)
96295 

Length

Max length85
Median length31
Mean length29.0579528
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39007 ?
Unique (%)31.4%

Sample

1st row8700 Research in Austin (TX)
2nd row8700 Research Blvd in Austin (TX)
3rd rowColony Creek And Hunters Trace in Austin (TX)
4th rowAustin (TX)
5th row12034 Research Blvd in Austin (TX)

Common Values

ValueCountFrequency (%)
Austin (TX)22851
 
18.4%
Travis (TX)1995
 
1.6%
Outside Jurisdiction1529
 
1.2%
7201 Levander Loop in Austin (TX)811
 
0.7%
Manor (TX)620
 
0.5%
Pflugerville (TX)608
 
0.5%
Del Valle (TX)526
 
0.4%
4434 Frontier Trl in Austin (TX)208
 
0.2%
124 W Anderson Ln in Austin (TX)192
 
0.2%
4434 Frontier Trail in Austin (TX)158
 
0.1%
Other values (53693)94603
76.2%

Length

2022-04-26T18:50:10.963824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx122667
17.8%
austin103184
 
15.0%
in95339
 
13.9%
and14760
 
2.1%
dr11637
 
1.7%
travis10973
 
1.6%
drive8391
 
1.2%
rd7651
 
1.1%
ln6320
 
0.9%
st6144
 
0.9%
Other values (16205)301298
43.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-26T18:45:33.651278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:41:46.485137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:42:41.666445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:43:38.170143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:44:35.093605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:46:08.771942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:41:46.621772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:42:41.801086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:43:38.309278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:44:35.273630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:46:43.484737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:41:46.747435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:42:41.937719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:43:38.442938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:44:35.471118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:47:21.549896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:41:46.868113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:42:42.067373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:43:38.574585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:44:35.659613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:48:05.180781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:41:47.081541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:42:42.288287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:43:38.804969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-26T18:44:36.010674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-26T18:50:11.075525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-26T18:50:11.197200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-26T18:50:11.309898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-26T18:50:11.432570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-26T18:50:11.581191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-26T18:50:06.241834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-26T18:50:06.789878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

animal_idnameanimal_typebreedcolorsexintake_typeintake_conditionneutered_intakeneutered_outcomeage_month_inage_month_outage_year_inage_year_outdatetime_indatetime_outoutcome_typeoutcome_subtypedays_in_shelterfound_location
0A006100ScampDogSpinone Italiano MixYellow/WhiteMalePublic AssistNormalyesyes72.072.06.06.02014-03-07 14:26:002014-03-08 17:10:00Return to OwnerUnknown1.118700 Research in Austin (TX)
1A006100ScampDogSpinone Italiano MixYellow/WhiteMalePublic AssistNormalyesyes84.084.07.07.02014-12-19 10:21:002014-12-20 16:35:00Return to OwnerUnknown1.268700 Research Blvd in Austin (TX)
2A006100ScampDogSpinone Italiano MixYellow/WhiteMaleStrayNormalyesyes120.0120.010.010.02017-12-07 14:07:002017-12-07 00:00:00Return to OwnerUnknown0.59Colony Creek And Hunters Trace in Austin (TX)
3A047759OreoDogDachshundTricolorMaleOwner SurrenderNormalyesyes120.0120.010.010.02014-04-02 15:55:002014-04-07 15:12:00TransferPartner4.97Austin (TX)
4A134067BanditDogShetland SheepdogBrown/WhiteMalePublic AssistInjuredyesyes192.0192.016.016.02013-11-16 09:02:002013-11-16 11:54:00Return to OwnerUnknown0.1212034 Research Blvd in Austin (TX)
5A141142BettieDogLabrador Retriever/Pit BullBlack/WhiteFemaleStrayAgedyesyes180.0180.015.015.02013-11-16 14:46:002013-11-17 11:40:00Return to OwnerUnknown0.87Austin (TX)
6A163459SashaDogMiniature Schnauzer MixBlack/GrayFemaleStrayNormalnono180.0180.015.015.02014-11-14 15:11:002014-11-14 19:28:00Return to OwnerUnknown0.18Ih 35 And 41St St in Austin (TX)
7A165752PepDogLhasa Apso MixBrown/WhiteMaleStrayNormalyesyes180.0180.015.015.02014-09-15 11:28:002014-09-15 16:35:00Return to OwnerUnknown0.21Gatlin Gun Rd And Brodie in Austin (TX)
8A169438UnknownBirdDove MixGray/WhiteUnknownStrayNormalUnknownUnknown216.0216.018.018.02018-04-04 20:37:002018-04-04 20:47:00RelocateUnknown0.01Dessau in Austin (TX)
9A178569BotiDogShetland Sheepdog MixWhite/BlackMalePublic AssistNormalyesyes180.0180.015.015.02014-03-17 09:45:002014-03-23 15:57:00Return to OwnerUnknown6.26Austin (TX)

Last rows

animal_idnameanimal_typebreedcolorsexintake_typeintake_conditionneutered_intakeneutered_outcomeage_month_inage_month_outage_year_inage_year_outdatetime_indatetime_outoutcome_typeoutcome_subtypedays_in_shelterfound_location
124091A830166ChiquitaDogLabrador Retriever MixTan/WhiteMaleStrayNormalnoyes12.08.01.00000.66672021-03-03 14:44:002021-01-27 11:18:00AdoptionUnknown35.14904 W Riverside in Austin (TX)
124092A830169UnknownDogPit BullBlack/WhiteFemaleStrayNormalnoyes24.08.02.00000.66672021-03-03 15:19:002021-01-28 17:05:00AdoptionUnknown33.939025 Williamson Rd in Travis (TX)
124093A830170UnknownDogPit BullBlack/WhiteMaleStrayNormalnono72.03.06.00000.25002021-03-03 15:19:002021-01-25 16:00:00TransferPartner36.979025 Williamson Rd in Travis (TX)
124094A830171UnknownDogPlott Hound MixBrown Brindle/WhiteFemaleStrayNormalnoyes12.024.01.00002.00002021-03-03 17:13:002021-02-06 14:30:00AdoptionUnknown25.111310 West Howard Lane in Austin (TX)
124095A830172SullyDogRottweiler/Great DaneBlack/TanMaleOwner SurrenderNormalnoyes24.02.02.00000.16672021-03-03 16:50:002021-01-25 18:25:00AdoptionUnknown36.93Austin (TX)
124096A830173UnknownDogCairn TerrierBrownMaleStrayNormalnono12.03.01.00000.25002021-03-03 15:59:002021-02-11 11:43:00TransferPartner20.1814912 Fagerquist Rd in Travis (TX)
124097A830174UnknownDogBlack Mouth CurBrown/BlackFemaleStrayNormalnoyes1.012.00.08331.00002021-03-03 15:59:002021-01-26 11:21:00TransferPartner36.1914912 Fagerquist Rd in Travis (TX)
124098A830180GigiDogAustralian Cattle Dog/Belgian MalinoisBrown Brindle/WhiteFemaleOwner SurrenderNormalnoyes108.084.09.00007.00002021-03-03 16:31:002021-01-27 14:24:00Return to OwnerUnknown35.09Austin (TX)
124099A830181NonaCatDomestic Shorthair MixWhite/BlackFemaleOwner SurrenderNormalyesyes48.036.04.00003.00002021-03-03 16:31:002021-01-25 13:19:00TransferSnr37.13Austin (TX)
124100A830183UnknownDogChihuahua ShorthairWhiteFemaleStrayMedicalnoUnknown1.07.00.08330.58332021-03-03 17:12:002021-01-30 16:35:00AdoptionFoster32.036802 Bryonwood Drive in Austin (TX)